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Spatio-temporal patterns and determinants of measles incidence in Ethiopia between 2018 and 2024

  • Habtamu Milkias Wolde
  • , Ali Raza
  • , Wakjira Kebede
  • , Beom Seok Jeong
  • , Yeongjae Kim
  • , Sunyeup Kim
  • , Jae June Dong
  • , Youngoh Bae
  • , Seung Won Lee
  • Sungkyunkwan University
  • Jimma University Ethiopia
  • Kangbuk Samsung Hospital
  • Korean Armed Forces Capital Hospital

Research output: Contribution to journal › Article › peer-review

Abstract

Background: Despite the availability of an effective vaccine, measles remains a major public health concern in Ethiopia, with recurrent outbreaks and substantial spatial heterogeneity. Understanding its spatio-temporal patterns and determinants is critical for optimizing control strategies and achieving elimination goals. Methods: A retrospective spatio-temporal analysis was conducted using national measles surveillance data from 2018-2024, aggregated at the zonal level. Geographic clustering was assessed using Moran's I, Getis-Ord Gi*, and Local Indicators of Spatial Association (LISA) statistics. A negative binomial regression model incorporating spatial and temporal effects was fitted to identify determinants of measles distribution, integrating epidemiological, environmental, nutritional, and socioeconomic variables. Results: Between 2018 and 2024, 71,635 measles cases were reported, with the highest burdens observed in Oromia, Somali, Southern Ethiopia, and parts of Amhara. Significant spatial clustering was detected (Moran's I = 0.154, p = 0.003), with persistent hotspots in southern and southwestern zones. The model showed that higher night-light intensity (IRR = 2.21, p < 0.001) and temporal (IRR = 1.24, p = 0.028) and spatial lag effects (IRR = 1.73, p < 0.001) were strongly associated with increased measles incidence. Higher temperature (IRR = 0.78, p = 0.005) and relative wealth index (IRR = 0.40, p < 0.001) were inversely associated, while underweight prevalence and distance to health facilities were not significant predictors of measles distribution. Conclusion: Measles transmission in Ethiopia exhibits clear spatial clustering and temporal persistence, strongly influenced by socioeconomic inequities, human concentration, and climatic conditions. Incorporating spatio-temporal modeling into routine surveillance can enhance early detection and guide geographically targeted immunization, nutrition, and equity-focused interventions toward measles elimination.

Original languageEnglish
Pages (from-to)1760450
Number of pages1
JournalFrontiers in Public Health
Volume14
DOIs
StatePublished - 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

Keywords

  • Ethiopia
  • hotspots
  • measles
  • spatial lags
  • spatio-temporal modeling
  • temporal lags
  • vaccination

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